Course Contents

Artificial Intelligence (AI): Introduction, History, and Evolution
Agents of Artificial Intelligence
Introduction to Fuzzy System (FS), Artificial Neural Network (ANN), Evolutionary Computing (EC), Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Algorithm (PSO), etc.
Machine Learning: Unsupervised Learning, Supervised Learning, Semi supervised Learning, Reinforcement Learning
Clustering and Biclustering: K-means, Fuzzy c-means (FCM), Self-organizing maps (SOM), and other Clustering Algorithms
Classification: Support Vector Machines (SVM), K Nearest Neighbour (KNN), ANN, Fuzzy Rule Based, and other Classifiers
Curve fitting, Regression models, Prediction/Forecasting: ANN and Fuzzy Rule Based Regression Models
Performance Measures for Clustering, Biclustering, Classification, and Regression Algorithms
Deep Learning and Transfer Learning: Deep Neural Networks (DNN), Fuzzy Neural Networks (FNN), etc.
Case studies in the areas of signal processing, computer vision, intelligent control, transportation, prognosis and health management, bioinformatics, etc.

Course Instructor:
Nishchal K. Verma (nishchal@iitk.ac.in)